Sr. Machine Learning Researcher, Domain-Aware Modeling & Scientific Machine Learning

At Bayer we're visionaries, driven to solve the world's toughest challenges and striving for a world where 'Health for all Hunger for none' is no longer a dream, but a real possibility.

We are seeking a Sr. Machine Learning Researcher with strong expertise in the mathematical foundations of machine learning and scientific computing to develop next-generation domain-aware models for agriculture.

Responsibilities:

• Scientific ML Model Development: Design, build, and validate domain-aware machine learning models that incorporate prior scientific knowledge into learning algorithms for agricultural and genomic applications.
• Mathematical Framework Design: Develop novel architectures and loss functions that embed biological constraints, conservation laws, symmetry properties, or known functional relationships into neural network training.
• Genomic Selection & Editing Enablement: Architect models that leverage high-dimensional genomic, phenomic, and environmental data to predict complex trait outcomes, identify causal genetic variants, and prioritize genome editing targets with quantified uncertainty.
• Uncertainty Quantification: Implement rigorous uncertainty quantification frameworks to provide decision-makers with calibrated confidence estimates on model predictions.
• Interdisciplinary Collaboration: Partner with geneticists, plant biologists, agronomists, environmental scientists, and software engineers to translate domain expertise into model architecture decisions and validate model outputs against biological ground truth.
• Scalable Deployment: Work with engineering and IT teams to transition research prototypes into production-grade models integrated within breeding and discovery pipelines.
• Research Contribution: Contribute to publications in leading venues, participate in the internal scientific community, and stay at the frontier of scientific machine learning methodology.
• Documentation & Communication: Prepare comprehensive technical documentation, present findings to both technical and non-technical stakeholders, and build organisational trust in AI-driven decision-making.

Requirements:

• PhD in a related quantitative discipline with demonstrated depth in mathematical modeling.
• Demonstrated research output in scientific machine learning, numerical methods for differential equations, or data-driven modeling of physical/biological systems.
• Proficiency in modern deep learning frameworks and scientific computing libraries.
• Experience formulating and solving problems involving high-dimensional, structured, or multi-modal data.
• Strong communication skills and willingness to collaborate across disciplines.

Preferred Qualifications:

• 5+ years post-PhD relevant experience.
• Demonstrated experience with domain-aware modeling paradigms.
• Experience with Bayesian inference, Gaussian processes, hierarchical models, or probabilistic programming.
• Familiarity with nonlinear dynamics, dynamical systems theory, or systems biology modeling.
• Background in surrogate modeling, model reduction, or multi-fidelity methods.
• Exposure to genomics data structures or quantitative genetics.
• Experience deploying ML models into production environments.
• Experience collaborating in interdisciplinary research teams.

Compensation:

• Salary: approximately $120k-170k.
• Additional compensation may include a bonus or incentive program.
• Benefits include health care, vision, dental, retirement, PTO, sick leave, etc.